arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1703.07697math.NAcs.NA
收藏

高效实现具有简化牛顿迭代的辛隐式龙格-库塔方案

Efficient implementation of symplectic implicit Runge-Kutta schemes with simplified Newton iterations

KZAA saila, Informatika Fakultatea, UPV/EHU(巴斯克地区大学信息学院)

Mikel Antoñana, Joseba Makazaga, Ander Murua

AI总结:

本文提出一种高效实现辛隐式龙格-库塔方案的方法,通过简化牛顿迭代减少线性系统求解的计算量和舍入误差。

摘要:

我们关注通过牛顿类迭代高效实现辛隐式龙格-库塔(IRK)方法以求解常微分方程组的问题。我们特别关注对称辛IRK方案(如具有高斯节点的插值方法)。对于用于求解d维常微分方程组的s阶段IRK方案,应用简化版本的牛顿迭代需要在每一步求解多个线性系统(每个迭代一个),这些线性系统具有相同的sd×sd实系数矩阵。我们提出将这样的sd维线性系统重写为等价的(s+1)d维系统,该系统可通过执行[s/2]+1个d×d实矩阵的LU分解来求解。我们提供了一种基于牛顿类迭代的C实现,用于具有高斯节点的龙格-库塔插值方法,该方法利用这种线性系统的重写,并特别注意减少舍入误差的影响。我们报告了一些数值实验,展示了我们实现的舍入误差传播减少。

英文摘要:

We are concerned with the efficient implementation of symplectic implicit Runge-Kutta (IRK) methods applied to systems of (non-necessarily Hamiltonian) ordinary differential equations by means of Newton-like iterations. We pay particular attention to symmetric symplectic IRK schemes (such as collocation methods with Gaussian nodes). For a $s$-stage IRK scheme used to integrate a $d$-dimensional system of ordinary differential equations, the application of simplified versions of Newton iterations requires solving at each step several linear systems (one per iteration) with the same $sd \times sd$ real coefficient matrix. We propose rewriting such $sd$-dimensional linear systems as an equivalent $(s+1)d$-dimensional systems that can be solved by performing the LU decompositions of $[s/2] +1$ real matrices of size $d \times d$. We present a C implementation (based on Newton-like iterations) of Runge-Kutta collocation methods with Gaussian nodes that make use of such a rewriting of the linear system and that takes special care in reducing the effect of round-off errors. We report some numerical experiments that demonstrate the reduced round-off error propagation of our implementation.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑